---
license: mit
language:
  - en
library_name: pytorch
pipeline_tag: robotics
tags:
  - robotics
  - vla
  - vision-language-action
  - libero
  - neurovla
  - spiking-neural-network
  - brain-inspired
base_model:
  - Qwen/Qwen2.5-VL-3B-Instruct
datasets:
  - LIBERO
---

# NeuroVLA · LIBERO (all 4 suites, joint training)

> Brain-inspired Vision-Language-Action (VLA) checkpoint released with
> the [AlphaBrain](https://github.com/AlphaBrainGroup/AlphaBrain)
> framework. Trained jointly on **all four LIBERO suites** — Goal,
> Spatial, Object, and Long — for direct evaluation across the full
> LIBERO benchmark without retraining.

NeuroVLA couples a Qwen2.5-VL backbone with a **layer-wise Q-Former**
that extracts action-relevant features from the VLM's hidden states,
feeding a **Spiking Neural Network (SNN)** action head. The model was
trained in a single supervised run (not continual learning) on a mixed
stream of all 4 LIBERO suites, using the `libero_all` data mix.

## Overview

| | |
|:---|:---|
| **Architecture**        | NeuroVLA (Qwen2.5-VL-3B + layer-wise Q-Former + SNN head) |
| **Base VLM**            | `Qwen/Qwen2.5-VL-3B-Instruct`                              |
| **Q-Former**            | Layers 36 → 37 · `num_query_tokens=8` · `output_dim=768`   |
| **Action head**         | DiT-based, `hidden_size=1024`, `action_dim=7`, `state_dim=7`, chunk 16 |
| **Training data**       | LIBERO · **all 4 suites (Goal + Spatial + Object + Long)** · `dataset_mix=libero_all` |
| **Training type**       | Supervised fine-tuning (single run; not continual learning) |
| **Attention**           | SDPA (not flash-attention, to avoid ABI pinning)           |
| **Optimiser**           | AdamW · `lr_base = 2.5e-5` · cosine-with-min-lr · 5 000 warmup |
| **Step budget**         | 50 000 (this release) · saved every 10 000 steps           |
| **Hardware / batch**    | 2 × A800 80 GB · `per_device_batch_size = 16`              |

## Files

```
├── README.md                   model card
├── framework_config.yaml       AlphaBrain framework configuration
├── dataset_statistics.json     action normalisation statistics (required for inference)
├── model.safetensors           full VLA weights (~7.7 GB)
├── resume_meta.json            training metadata (step count, GPU count)
└── qwen_pretrained/            Qwen2.5-VL tokenizer + preprocessor configs
```

## Usage

```bash
git clone https://github.com/AlphaBrainGroup/AlphaBrain.git
cd AlphaBrain
pip install -e .

export PRETRAINED_MODELS_DIR=/path/to/models   # must contain Qwen2.5-VL-3B-Instruct/

huggingface-cli download AlphaBrainGroup/neurovla-libero-all4suite \
    --local-dir ./neurovla_libero_all

python deployment/model_server/server_policy.py \
    --ckpt_path ./neurovla_libero_all --port 10093 --use_bf16
```

For evaluation on any of the 4 LIBERO suites, see the
[LIBERO eval pipeline](https://github.com/AlphaBrainGroup/AlphaBrain/tree/dev/benchmarks/LIBERO/eval).

## Reproduction

```bash
# Framework's NeuroVLA pretraining entry
bash scripts/run_brain_inspired_scripts/run_neurovla_pretrain.sh \
    --yaml configs/neurovla_all4suite_libero.yaml   # (or equivalent config for 4-suite mix)
```

Expect multi-day training on 2 × A800 80 GB for the full 50 000-step
schedule. The shipped `framework_config.yaml` is the exact training
configuration used for this checkpoint.

## Notes

- **Joint-training baseline**, not continual learning. For the CL
  release of NeuroVLA (sequential training on LIBERO-Goal with
  Experience Replay), see
  [`AlphaBrainGroup/neurovla-cl-libero-goal`](https://huggingface.co/AlphaBrainGroup/neurovla-cl-libero-goal)
  and its LoRA variant.
- **Attention implementation is SDPA**, chosen to avoid flash-attn ABI
  pinning across environments. Users who have a matching flash-attn
  wheel can override via `--framework.qwenvl.attn_implementation=flash_attention_2`.

## License

MIT — see the [parent repository](https://github.com/AlphaBrainGroup/AlphaBrain).

## Citation

```bibtex
@misc{alphabrain2026,
  title  = {AlphaBrain: A Modular Open-Source Framework for Embodied Intelligence Research},
  author = {AlphaBrain Team},
  year   = {2026},
  url    = {https://github.com/AlphaBrainGroup/AlphaBrain}
}
```